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On the Phase Transition Phenomenon of Graph Laplacian Eigenfunctions on Trees (Recent development and scientific applications in wavelet analysis)

机译：图拉普拉斯特征函数在树上的相变现象（小波分析的最新发展和科学应用）

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Saito Naoki; Woei Ernest;
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年度 2011
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正文语种 en
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1. Tree simplification and the 'plateaux' phenomenon of graph Laplacian eigenvalues [J] . Saito Naoki, Woei Ernest Linear Algebra and its Applications . 2015,第Null期

机译：树简化和图拉普拉斯特征值的“平稳”现象
2. Spectral analysis of the transition operator and its applications to smoothness analysis of wavelets [J] . Jia RQ., Jiang QT. SIAM Journal on Matrix Analysis and Applications . 2003,第4期

机译：过渡算子的谱分析及其在小波平滑度分析中的应用
3. Application of the wavelet transform in the laminar turbulent transition for a flow in a mixed convection phenomenon [J] . C. Abid, C. Barberi Moine, F. Papini The European physical journal, B. Condensed matter physics . 2000,第4期

机译：小波变换在混合对流现象的层流湍流过渡中的应用
4. Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions [C] . Sridhar Mahadevan, Mauro Maggioni Annual Conference on Neural Information Processing Systems . 2006

机译：与扩散小波和拉普拉斯特征函数的价值函数近似
5. Local computation and estimation of wavelet coefficients in the dual-tree complex wavelet transform and its applications [D] . El-Shehaby, Iman A. 2009

机译：双树复小波变换中小波系数的局部计算与估计及其应用
6. Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography [O] . Kenji Suzuki, Jun Zhang, Jianwu Xu -1

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7. On the new family of wavelet interpolating to the Shannon wavelet (Recent development and scientific applications in wavelet analysis) [O] . Fukuda Naohiro, Kinoshita Tamotu 2011

机译：关于小波插值到Shannon小波的新族（小波分析的最新发展和科学应用）
8. Spectral Analysis of the Laplacian with a Discontinuous Coefficient in a Half-Space and the Associated Eigenfunction Expansion. [R] . wilcox,calvin h. 1973

机译：半空间不连续系拉普拉斯算子的谱分析及相关特征函数展开。

On the Phase Transition Phenomenon of Graph Laplacian Eigenfunctions on Trees (Recent development and scientific applications in wavelet analysis)

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